DLEJena: A practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet

This paper describes DLEJena, a practical reasoner for the OWL 2 RL profile that combines the forward-chaining rule engine of Jena and the Pellet DL reasoner. This combination is based on rule templates, instantiating at run-time a set of ABox OWL 2 RL/RDF Jena rules dedicated to a particular TBox that is handled by Pellet. The goal of DLEJena is to handle efficiently, through instantiated rules, the OWL 2 RL ontologies under direct semantics, where classes and properties cannot be at the same time individuals. The TBox semantics are treated by Pellet, reusing in that way efficient and sophisticated TBox DL reasoning algorithms. The experimental evaluation shows that DLEJena achieves more scalable ABox reasoning than the direct implementation of the OWL 2 RL/RDF rule set in the Jena’s production rule engine, which is the main target of the system. DLEJena can be also used as a generic framework for applying an arbitrary number of entailments beyond the OWL 2 RL profile.     

A Flexible Ontology Reasoning Architecture for the Semantic Web

Knowledge-based systems in the semantic Web era can make use of the power of the semantic Web languages and technologies, in particular those related to ontologies. Recent research has shown that user-defined data types are very useful for semantic Web and ontology applications. The W3C semantic Web best practices and development working group has set up a task force to address this issue. Very recently, OWL-Eu and OWL-E, two decidable extensions of the W3C standard ontology language OWL DL, have been proposed to support customized data types and customized data type predicates, respectively. In this paper, we propose a flexible reasoning architecture for these two expressive semantic Web ontology languages and describe our prototype implementation of the reasoning architecture, based on the well-known FaCT DL reasoner, which witnesses the two key flexibility features of our proposed architecture: 1) It allows users to define their own data types and data type predicates based on built-in ones and 2) new data type reasoners can be added into the architecture without having to change the concept reasoner     

Two learning approaches for a rule-based intuitive reasoner

This paper is a discussion of two continuous learning approaches for improving classification accuracy for an intuitive reasoner algorithm. The reasoner predicted the value of a given target variable by multiple iterations of forward-chained, rule-based inference. Each rule in the reasoner’s rule set had associated with it a weight, referred to here as “Strength of Belief” (SB). The value of SB of a rule indicated the certainty level of that rule. In each iteration of reasoning, any instances of similar values for a given variable were replaced by a single consolidated datum and the SB associated with the consolidated datum was increased. At the end of the reasoning process, the class (value) of the target variable which had the highest SB was reported as the conclusion. The rule set for the reasoner was generated based on a training data set that contained 80% of the data in a weather database comprising 50 years worth of hourly measurements for 54 weather variables. Each rule was induced based on only a small subset of the weather data. The intuitive reasoner was tested by using the induced rules to predict a number of pre-selected target variables using 275 test cases created from the test data. The first continuous learning approach was to identify relevant input variables for the reasoner, and the second was to rebalance the rule set used by the reasoner by adjusting the SB associated with each of the rules. Because of the way the rules were induced, the resulting rules did not contain any information about the relevance of the 53 possible input variables to the task of predicting a given target variable for previously unseen cases. A method was developed to identify which input variables were most relevant to the task based on the induced rule set. This method resulted in higher prediction accuracy of the intuitive reasoner than using a set of randomly chosen input variables for four of six target variables. The second continuous learning approach was intended to address the class imbalance problem in the rule set. The intuitive reasoner appeared to over-fit classes (values) which had frequent representation in the rule set. To address this problem, a heuristic was developed that generated adjustment factors for the SB values of the rules. The use of this heuristic improved the classification accuracy of the intuitive reasoner for four of the six target variables.     

基于UML的管理信息系统开发

以一个管理信息系统为背景,探讨了利用统一建模语言对系统进行面向对象分析、设计以及实现问题,顺利实现了对象模型向关系数据库模型的转换。     

A Rule-Based Object-Oriented OWL Reasoner

In this paper, we describe O-DEVICE, a memory-based knowledge-based system for reasoning and querying OWL ontologies by implementing RDF/OWL entailments in the form of production rules in order to apply the formal semantics of the language. Our approach is based on a transformation procedure of OWL ontologies into an object-oriented schema and the application of inference production rules over the generated objects in order to implement the various semantics of OWL. In order to enhance the performance of the system, we introduce a dynamic approach of generating production rules for ABOX reasoning and an incremental approach of loading ontologies. O-DEVICE is built over the CLIPS production rule system, using the object-oriented language COOL to model and handle ontology concepts and RDF resources. One of the contributions of our work is that we enable a well-known and efficient production rule system to handle OWL ontologies. We argue that although native OWL rule reasoners may process ontology information faster, they lack some of the key features that rule systems offer, such as the efficient manipulation of the information through complex rule programs. We present a comparison of our system with other OWL reasoners, showing that O-DEVICE can constitute a practical rule environment for ontology manipulation.     

Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes

Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality. However, the other two suites have not been heavily validated except by their original proposers. Here, we explore the ability of these three metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error-prone classes. We also conclude that the class components in the MOOD metrics suite are not good class fault-proneness predictors. Analyzing multivariate binary logistic regression models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes.     

可视化产生式系统的设计与实现

产生式系统是最流行的专家系统类型。目前常见的产生式系统,如OPS5和CLIPS,它们的应用和开发界面都是类似LISP的文本界面,可操作性差。文章基于面向对象的方法和技术,设计并实现了一个可视化的产生式系统,该系统采用Rete算法进行推理,提供了事实库、规则库和Rete网络的可视化的维护功能。实际运行表明,该系统效率高,可操作性强。     

蜜桔种植专家系统的设计与实现

介绍使用CLIPS工具开发蜜桔种植专家系统。该专家系统具有图片、影像等多媒体推理能力。核心组件包括知识库、规则表、推理机。推理机采用软件虚拟机的方式运行。该系统能够提高蜜桔的产量和减少病虫害的发生,对农业现代化具有积极意义。     

Verifying feature models using OWL

Feature models are widely used in domain engineering to capture common and variant features among systems in a particular domain. However, the lack of a formal semantics and reasoning support of feature models has hindered the development of this area. Industrial experiences also show that methods and tools that can support feature model analysis are badly appreciated. Such reasoning tool should be fully automated and efficient. At the same time, the reasoning tool should scale up well since it may need to handle hundreds or even thousands of features a that modern software systems may have. This paper presents an approach to modeling and verifying feature diagrams using Semantic Web OWL ontologies. We use OWL DL ontologies to precisely capture the inter-relationships among the features in a feature diagram. OWL reasoning engines such as FaCT++ are deployed to check for the inconsistencies of feature configurations fully automatically. Furthermore, a general OWL debugger has been developed to tackle the disadvantage of lacking debugging aids for the current OWL reasoner and to complement our verification approach. We also developed a CASE tool to facilitate visual development, interchange and reasoning of feature diagrams in the Semantic Web environment.     

基于CLIPS的卫星任务规划专家系统设计

本文提出了基于CLIPS的卫星任务规划专家系统的设计方法,详细分析了系统的结构和功能,重点讨论了中文产生式系统的BNF范式、基于上下文的推理机制和集合运算符。中文产生式系统的BNF范式基于CLIPS标准BNF范式定义,并依据BNF范式进行规则表示和规则自定义获取;推理机采用上下文限制的规则控制策略,依据不同的上下文加载相关的事实和规则,提高推理机的运行效率;利用规则中的对象逻辑子式进行了集合运算符的设计,并对极值运算符、属性差值运算符和均值运算符等三类集合运算符进行了探讨。该系统解决了卫星任务规划中知识表示和知识获取问题,提高了卫星任务规划推理效率,为卫星任务规划人员提供有效的辅助决策功能。     

An intelligent database for engineering applications

Many engineering applications need to response to unpredictable events in a timely manner. Active database systems provide an event-driven rule processing capability to meet this requirement. In this paper, we present an intelligent database which integrates an object-oriented database (OODB) with an expert system, CLIPS. The paper describes the design and implementation of the rule manager of this intelligent database. In the rule manager, event-condition-action (ECA) rules are represented as first class objects of the OODB. A rule definition language (RDL) has been developed to manipulate ECA rules in a declarative way. A graphical user interface (GUI) also supplies a template to interactively define, delete, update and check ECA rules. Detection of time events, method events, absolute events and composite events is supported by the rule manager. The CLIPS inference engine is used to control condition evaluation and action execution after an ECA rule is triggered. Finally, a typical workflow application is used to illustrate the functionality of the system.     

基于Object-Z多态推理

Object-Z是形式规格说明语言Z的面向对象扩充,基于严格的集合论与数理逻辑,具有面向对象的特点：类、对象、继承、封装与多态等。用它可以精确描述大型软件需求规格说明,且能够进行严密的逻辑推理与验证。本文主要探讨了它的多态性推理,给出了相应的推理规则与方法,可以推理出Object-Z的多态行为,并着重体现推理的重用。     

Design of object-oriented database for the definition of machining operation sequences of 3D workpieces

This paper describes the activities of Object-Oriented (OO) analysis that were implemented in order to obtain a high part representation level and to give sets of structured and hierarchical data to the Computer Aided Process Planning (CAPP) system. The OO modeling activities were carried out by using the Object-Oriented System Analysis (OOSA) method which allows careful specification of all the information contained inside the system. All the models used by this method have been described in detail to show how the OO database is defined and how it can be used by a generative CAPP system. The feature model proposed is defined by taking all the part information that can be recognized and extracted from the Computer Aided Design (CAD) model into account. The result is the design of an OO database which allows the CAPP system to use manufacturing features to define machining operation sequences of 3D workpieces. The approach proposed is generic enough to integrate any geometrical forms which can be recognized and identified from the CAD system. Hole geometry is taken as an example to show the link between the step of OO analysis and the step of knowledge representation in the Expert System which has been used to generate machining cycles. The OO database presented makes up a real solution of CAD/CAPP/CAM integration by using feature modeling.     

结合CLIPS和VC++实现规则推理的方法

专家系统开发通常有三种方式：高级程序语言,专家系统外壳,专家系统工具。该文介绍了专家系统工具CLIPS6和高级程序语言VC++6.0的各自特点,提出了将专家系统工具CLIPS6与VC＋＋6.0高级语言编程工具结合起来开发专家系统,实现规则推理。详细描述了CLIPS嵌入VC++的一般过程：如何把CLIPS6嵌入VC++,如何加入CLIPS用户自定义函数来传递和返回参数。并以摩托车智能设计为例,详细阐述了规则编辑,事实获取,实现规则解释,实现人机交互功能等,从而实现摩托车智能设计的规则推理。     

基于CLIPS的智能无人飞艇故障诊断专家系统开发

针对智能无人飞艇的故障诊断问题,设计开发了一套基于CLIPS框架的故障诊断专家系统。首先,根据诊断专家的知识进行故障分类并建立故障树;其次,基于CLIPS工具设计了智能无人飞艇的故障事实库和规则库。然后采用静态链接的方式将CLIPS框架嵌入到C++中,并设计了"路由跳转"功能,实现了用户输入与CLIPS的数据交换接口,并利用MFC框架开发了相应人机交互界面。该智能无人飞艇故障诊断专家系统的开发,改善了现阶段人工故障诊断的不规范及效率低下等问题,为智能无人飞艇的故障诊断、分析和排除提供了平台和支持技术。